Search Results for author: Wenda Zhou

Found 9 papers, 8 papers with code

Vitruvion: A Generative Model of Parametric CAD Sketches

no code implementations ICLR 2022 Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams

Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards.

Autobahn: Automorphism-based Graph Neural Nets

1 code implementation NeurIPS 2021 Erik Henning Thiede, Wenda Zhou, Risi Kondor

Our formalism also encompasses novel architectures: as an example, we introduce a graph neural network that decomposes the graph into paths and cycles.

SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design

1 code implementation16 Jul 2020 Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams

Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design.

Program Synthesis

Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions

1 code implementation3 Mar 2020 Kamiar Rahnama Rad, Wenda Zhou, Arian Maleki

We study the problem of out-of-sample risk estimation in the high dimensional regime where both the sample size $n$ and number of features $p$ are large, and $n/p$ can be less than one.


Discrete Object Generation with Reversible Inductive Construction

1 code implementation NeurIPS 2019 Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams

The success of generative modeling in continuous domains has led to a surge of interest in generating discrete data such as molecules, source code, and graphs.


Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions

2 code implementations ICML 2018 Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni

Consider the following class of learning schemes: $$\hat{\boldsymbol{\beta}} := \arg\min_{\boldsymbol{\beta}}\;\sum_{j=1}^n \ell(\boldsymbol{x}_j^\top\boldsymbol{\beta}; y_j) + \lambda R(\boldsymbol{\beta}),\qquad\qquad (1) $$ where $\boldsymbol{x}_i \in \mathbb{R}^p$ and $y_i \in \mathbb{R}$ denote the $i^{\text{th}}$ feature and response variable respectively.

Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data

1 code implementation27 Jun 2018 Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz

We solve this problem using recent ideas from graph sampling theory to (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this empirical risk that are automatically unbiased.

Graph Sampling Node Classification

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